The second environment simulates a 2D navigation scenario. The maps are created from gmapping [1]} outputs in real world locations to simulate real world navigation as shown in figure 4.
The goal in this environment is to reach a random target location in the map. The agent is given a full state observation as well as a demonstration during training.
The state-of-the-art (SOTA)[2]} technique treated this multi-environment navigation problem as a multi-task learning scenario, using the camera view rather than the map as input.
Nevertheless, the action space and the quantized state space remain the same as in the original paper.
In both environments, we focus on two evaluation metrics: the task success rate measures how many episodes end in the goal being successfully reached. The average episode length measures how quickly the agent was able to achieve it's goal.
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